CN105354638A - Prediction method and system for repair and maintenance costs of automobile - Google Patents
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Abstract
The present invention relates to a prediction method and system for repair and maintenance costs of an automobile. The method comprises: acquiring a model of a to-be-predicted automobile; searching automobile data that is as same as the model of the to-be-predicted automobile in a database according to the model of the to-be-predicted automobile to obtain a first search result; searching automobiles that meet the first condition and the second condition in the first search result, and according to the automobiles which meet the first condition and the second condition, calculating a repair and maintenance cost X based on the automobile age and a repair and maintenance cost Y based on the automobile mileage of the to-be-predicted automobile; and and fusing X and Y to obtain the repair and maintenance costs of the to-be-predicted automobile within the next n months. According to the prediction method and system for repair and maintenance costs of automobile, the repair and maintenance costs of the automobile can be predicted accurately.
Description
Technical field
The present invention relates to automobile maintenance maintenance, particularly relate to a kind of auto repair upkeep cost Forecasting Methodology and system.
Background technology
The normal operation of fleet's (such as passenger traffic, shipping, logistics, public affair etc.) has very important stable and facilitation to the national economic development, and the maintenance cost of fleet is in fleet's cost very important one.If fleet manager can predict the maintenance cost of following a period of time (such as next month, next season, next year etc.) exactly, so the financial planning of fleet will be more reasonable, and the operation of fleet also can be more steady.There are various software or APP can be used as analysis report to the history maintenance charge of fleet in the market, but all less than the prediction for maintenance cost.
Summary of the invention
Technical matters to be solved by this invention is to provide a kind of auto repair upkeep cost Forecasting Methodology and system, can carry out Accurate Prediction to the maintenance expense of automobile.
The technical scheme that the present invention solves the problems of the technologies described above is as follows: a kind of auto repair upkeep cost Forecasting Methodology, comprises the following steps:
S1, obtains the model of automobile to be measured;
S2, searches for the car data identical with automobile model to be measured in a database according to the model of automobile to be measured, obtains the first Search Results;
S3, in described first Search Results, search meets the vehicle of first condition and second condition respectively, and calculates automobile to be measured based on the maintenance expense X at vehicle age with repair upkeep cost Y based on vehicle mileage according to the vehicle meeting first condition and second condition respectively;
If need predict the maintenance expense of automobile to be measured in following n month, and the history of setting out on a journey of described automobile to be measured is m month, then described first condition refers to set out on a journey the vehicle that history is greater than m+n month;
If what automobile to be measured was current set out on a journey, mileage is L1, need predict that automobile to be measured is in the maintenance expense of mileage needed for L1 to L2, then the described second condition mileage that refers to set out on a journey is greater than L2, and all makees the vehicle of maintenance at the mileage points L4 being greater than L2 and the mileage points L3 that is less than L1;
S4, merges X and Y, obtains the maintenance expense that described vehicle to be predicted is required in following n month.
The invention has the beneficial effects as follows: search for the automobile identical with automobile model to be measured in a database according to the model of automobile to be measured, and again search for associated vehicle with set out on a journey history and mileage number in a database for search condition respectively, the maintenance expense of automobile to be measured based on vehicle age and mileage is calculated respectively according to the maintenance expense of these associated vehicles, finally the maintenance expense based on vehicle age and mileage is merged, obtain the maintenance expense that vehicle to be predicted is required in following n month.
On the basis of technique scheme, the present invention can also do following improvement:
Further, described step S1 also comprises: the year built obtaining automobile to be measured;
Described step S2 also comprises: search for the same model vehicle identical with the automobile making time to be measured in a database according to the year built, and judges whether the vehicle fleet size searched reaches threshold value t, if do not reach, then reformulates search condition search; If reach, then perform S3.
The beneficial effect of above-mentioned further scheme is adopted to be: to be limited Search Results by the year built, the similarity of vehicle and the vehicle to be measured searched in a database is increased, thus improves the precision of prediction of vehicle to be predicted maintenance expense required in following n month.
Further, reformulate search condition described in refer to: with the described immediate year built in Rail car manufacture time to be predicted or reformulate search condition with the model of the immediate vehicle of described vehicle model to be measured.
Further, calculate automobile to be measured according to the vehicle meeting first condition in described step S3 to be specially based on the maintenance expense X at vehicle age: obtain each vehicle meeting first condition the maintenance expense summation xi of m+1 month to m+n month, i for described in meet the numbering of the vehicle of first condition;
To the m+n maintenance expense summation xi of individual month, weighted mean value was got at m+1 month to all vehicles meeting first condition, obtains X;
Calculate automobile to be measured according to the vehicle meeting second condition in described step S3 to be specially based on the maintenance expense Y of vehicle mileage:
Obtain each vehicle meeting second condition at the maintenance expense of mileage points L3 and L4 and yj;
The maintenance expense of automobile to be measured based on mileage is calculated in the maintenance expense of mileage points L3 and L4 and yj: Yj=yj* (L2-L1)/(L4-L3) according to each vehicle meeting second condition;
The maintenance expense Yj based on mileage of the automobile to be measured calculated at maintenance expense and the yj of mileage points L3 and L4 the vehicle meeting second condition according to each gets weighted mean value, obtains Y.
The beneficial effect of above-mentioned further scheme is adopted to be: by calculating the maintenance expense based on vehicle age and mileage to multiple and that vehicle to be measured is similar vehicle, again multiple maintenance expense based on vehicle age and mileage is got weighted mean value respectively, thus improve the precision of prediction of vehicle to be predicted maintenance expense required in following n month.
Further, described step S5 is specially:
Get mean value or the weighted mean value of X and Y, as the maintenance expense that described vehicle to be predicted is required in following n month.
Adopting the beneficial effect of above-mentioned further scheme to be: to carry out integrated forecasting by treating measuring car from two aspects, the precision of prediction of vehicle to be predicted maintenance expense required in following n month can be improved.
The another kind of technical scheme that the present invention solves the problems of the technologies described above is as follows: a kind of auto repair upkeep cost prognoses system, comprising:
Acquisition module, for obtaining the model of automobile to be measured;
First search module, for searching for the car data identical with automobile model to be measured in a database according to the model of automobile to be measured, obtains the first Search Results;
Second search module, in described first Search Results, search meets the vehicle of first condition and second condition respectively, and calculates the maintenance expense X of automobile to be measured based on the vehicle age and the maintenance expense Y based on vehicle mileage according to the vehicle meeting first condition and second condition respectively;
If need predict the maintenance expense of automobile to be measured in following n month, and the history of setting out on a journey of described automobile to be measured is m month, then described first condition refers to set out on a journey the vehicle that history is greater than m+n month;
If what automobile to be measured was current set out on a journey, mileage is L1, need predict that automobile to be measured is in the maintenance expense of mileage needed for L1 to L2, then the described second condition mileage that refers to set out on a journey is greater than L2, and all makees the vehicle of maintenance at the mileage points L4 being greater than L2 and the mileage points L3 that is less than L1;
Fusion Module, for being merged by X and Y, obtains the maintenance expense that described vehicle to be predicted is required in following n month.
The invention has the beneficial effects as follows: search for the automobile identical with automobile model to be measured in a database by the first search module, and in a database again search for associated vehicle with set out on a journey history and mileage number for search condition by the second search module, the maintenance expense of automobile to be measured based on vehicle age and mileage is calculated respectively according to the maintenance expense of these associated vehicles, finally by Fusion Module, the maintenance expense based on vehicle age and mileage is merged, obtain the maintenance expense that vehicle to be predicted is required in following n month.
On the basis of technique scheme, the present invention can also do following improvement:
Further, described acquisition module also for, obtain the year built of automobile to be measured;
Described first search module also for, search for the same model vehicle identical with the automobile making time to be measured in a database according to the year built, and judge whether the vehicle fleet size that searches reaches threshold value t, if do not reach, then reformulates search condition search; If reach, then the second search module is searched for.
The beneficial effect of above-mentioned further scheme is adopted to be: to be limited Search Results by the year built, the similarity of vehicle and the vehicle to be measured searched in a database is increased, thus improves the precision of prediction of vehicle to be predicted maintenance expense required in following n month.
Further, described first search module also for: with the described immediate year built in Rail car manufacture time to be predicted or reformulate search condition with the model of the immediate vehicle of described vehicle model to be measured.
Further, described second search module also for:
Obtain each vehicle meeting first condition the maintenance expense summation xi of m+1 month to m+n month, i for described in meet the numbering of the vehicle of first condition;
To the m+n maintenance expense summation xi of individual month, weighted mean value was got at m+1 month to all vehicles meeting first condition, obtains X;
With
Obtain each vehicle meeting second condition at the maintenance expense of mileage points L3 and L4 and yj;
The maintenance expense of automobile to be measured based on mileage is calculated in the maintenance expense of mileage points L3 and L4 and yj: Yj=yj* (L2-L1)/(L4-L3) according to each vehicle meeting second condition;
The automobile to be measured calculated at maintenance expense and the yj of mileage points L3 and L4 the vehicle meeting second condition according to each gets weighted mean value based on the maintenance expense Yj of mileage, obtains Y.
The beneficial effect of above-mentioned further scheme is adopted to be: by calculating the maintenance expense based on vehicle age and mileage to multiple and that vehicle to be measured is similar vehicle, again multiple maintenance expense based on vehicle age and mileage is got weighted mean value respectively, thus improve the precision of prediction of vehicle to be predicted maintenance expense required in following n month.
Further, described Fusion Module also for, get mean value or the weighted mean value of X and Y, as the maintenance expense that described vehicle to be predicted is required in following n month.
Adopting the beneficial effect of above-mentioned further scheme to be: to carry out integrated forecasting by treating measuring car from two aspects, the precision of prediction of vehicle to be predicted maintenance expense required in following n month can be improved.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of a kind of auto repair upkeep cost of the present invention Forecasting Methodology;
Fig. 2 is the structural representation of a kind of auto repair upkeep cost of the present invention prognoses system.
Embodiment
Be described principle of the present invention and feature below in conjunction with accompanying drawing, example, only for explaining the present invention, is not intended to limit scope of the present invention.
As shown in Figure 1, a kind of auto repair upkeep cost Forecasting Methodology, comprises the following steps:
S1, obtains the model of automobile to be measured;
Described step S1 also comprises: the year built obtaining automobile to be measured;
S2, searches for the car data identical with automobile model to be measured in a database according to the model of automobile to be measured, obtains the first Search Results;
Described step S2 also comprises: search for the same model vehicle identical with the automobile making time to be measured in a database according to the year built, and judges whether the vehicle fleet size searched reaches threshold value t, if do not reach, then reformulates search condition search, if reach, then perform S3, described search condition of reformulating refers to: with the described immediate year built in Rail car manufacture time to be predicted or reformulate search condition with the model of the immediate vehicle of described vehicle model to be measured, such as: if automobile to be measured is 2010FordEscape, the search condition then reformulated is 2009FordEscape and 2011FordEscape, 2008FordEscape and 2012FordEscape, 2007FordEscape and 2013FordEscape, if can not meet the demands according to the vehicle fleet size that the year built searches, other vehicles under Ford can be searched for, such as: FordEdge, until the vehicle fleet size searched is greater than threshold value t, t value is 10 just can to meet the demands, but t also can get larger value to reach higher precision of prediction.
S3, in described first Search Results, search meets the vehicle of first condition and second condition respectively, and calculates the maintenance expense X of automobile to be measured based on the vehicle age and the maintenance expense Y based on vehicle mileage according to the vehicle meeting first condition and second condition respectively;
If need predict the maintenance expense of automobile to be measured in following n month, and the history of setting out on a journey of described automobile to be measured is m month, then described first condition refers to set out on a journey the vehicle that history is greater than m+n month;
If what automobile to be measured was current set out on a journey, mileage is L1, need predict that automobile to be measured is in the maintenance expense of mileage needed for L1 to L2, then the described second condition mileage that refers to set out on a journey is greater than L2, and all makees the vehicle of maintenance at the mileage points L4 being greater than L2 and the mileage points L3 that is less than L1;
Calculate automobile to be measured according to the vehicle meeting first condition in described step S3 to be specially based on the maintenance expense X at vehicle age:
Obtain each vehicle meeting first condition the maintenance expense summation xi of m+1 month to m+n month, i for described in meet the numbering of the vehicle of first condition;
To the m+n maintenance expense summation xi of individual month, weighted mean value was got at m+1 month to all vehicles meeting first condition, obtains X; The weights coefficient used in weighted mean is determined by the similarity of the vehicle meeting first condition searched out with vehicle to be measured, the larger then weights of similarity are larger, and weight computing formula is: weights=1-0.2* (Rail car manufacture to be measured time-meet year built of the vehicle of first condition); Such as: if automobile to be measured is 2010FordEscape, then the weights of the vehicle of all 2010FordEscape are 1, the weights of the vehicle of all 2009FordEscape are 0.8, and the weights of all 2008FordEscape vehicles are 0.6;
Calculate automobile to be measured according to the vehicle meeting second condition in described step S3 to be specially based on the maintenance expense Y of vehicle mileage:
Obtain each vehicle meeting second condition at the maintenance expense of mileage points L3 and L4 and yj;
The maintenance expense of automobile to be measured based on mileage is calculated in the maintenance expense of mileage points L3 and L4 and yj: Yj=yj* (L2-L1)/(L4-L3) according to each vehicle meeting second condition;
The maintenance expense Yj based on mileage of the automobile to be measured calculated at maintenance expense and the yj of mileage points L3 and L4 the vehicle meeting second condition according to each gets weighted mean value, obtains Y; The weights coefficient that the weighted mean related to when calculating X and calculating Y uses is determined by the similarity of the vehicle meeting second condition searched out with vehicle to be measured, the larger then weights of similarity are larger, and weight computing formula is: weights=1-0.2* (Rail car manufacture to be measured time-meet year built of the vehicle of second condition); Such as: if automobile to be measured is 2010FordEscape, then the weights of the vehicle of all 2010FordEscape are 1, the weights of the vehicle of all 2009FordEscape are 0.8, and the weights of all 2008FordEscape vehicles are 0.6;
S4, merges X and Y, obtains the maintenance expense that described vehicle to be predicted is required in following n month.The fusion related in step S4 comprises averages or weighted mean value, the value of the weight coefficient related to when getting weighted mean value in step S4 is calculated with the ratio of the vehicle fleet meeting the first search condition and the second search condition simultaneously by the number of vehicles meeting the first search condition and the second search condition respectively, the car such as meeting the first search condition has 15, the car meeting the second search condition has 10, so the weight of X is 15/ (15+10), the weight of Y is 10/ (15+10), in addition, the weight coefficient related to when getting weighted mean value in step S4 also can optimize weighted value by training data and machine learning.
As shown in Figure 2, a kind of auto repair upkeep cost prognoses system, comprising:
Acquisition module, for obtaining model and the year built of automobile to be measured;
First search module, for searching for the car data identical with automobile model to be measured in a database according to the model of automobile to be measured, obtains the first Search Results; And also for, search for the same model vehicle identical with the automobile making time to be measured in a database according to the year built, and judge whether the vehicle fleet size that searches reaches threshold value t, if do not reach, then reformulates search condition search; If reach, then the second search module is searched for; Described first search module also for: with the described immediate year built in Rail car manufacture time to be predicted or reformulate search condition with the model of the immediate vehicle of described vehicle model to be measured.
Second search module, in described first Search Results, search meets the vehicle of first condition and second condition respectively, and calculates the maintenance expense X of automobile to be measured based on the vehicle age and the maintenance expense Y based on vehicle mileage according to the vehicle meeting first condition and second condition respectively;
If need predict the maintenance expense of automobile to be measured in following n month, and the history of setting out on a journey of described automobile to be measured is m month, then described first condition refers to set out on a journey the vehicle that history is greater than m+n month;
If what automobile to be measured was current set out on a journey, mileage is L1, need predict that automobile to be measured is in the maintenance expense of mileage needed for L1 to L2, then the described second condition mileage that refers to set out on a journey is greater than L2, and all makees the vehicle of maintenance at the mileage points L4 being greater than L2 and the mileage points L3 that is less than L1;
Described second search module also for:
Obtain each vehicle meeting first condition the maintenance expense summation xi of m+1 month to m+n month, i for described in meet the numbering of the vehicle of first condition;
To the m+n maintenance expense summation xi of individual month, weighted mean value was got at m+1 month to all vehicles meeting first condition, obtains X;
With
Obtain each vehicle meeting second condition at the maintenance expense of mileage points L3 and L4 and yj;
The maintenance expense of automobile to be measured based on mileage is calculated in the maintenance expense of mileage points L3 and L4 and yj: Yj=yj* (L2-L1)/(L4-L3) according to each vehicle meeting second condition;
The automobile to be measured calculated at maintenance expense and the yj of mileage points L3 and L4 the vehicle meeting second condition according to each gets weighted mean value based on the maintenance expense Yj of mileage, obtains Y.
Fusion Module, merge for X and Y is got, obtain the maintenance expense that described vehicle to be predicted is required in following n month, merge the mean value or the weighted mean value that refer to X and Y, as the maintenance expense that described vehicle to be predicted is required in following n month.
The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. an auto repair upkeep cost Forecasting Methodology, is characterized in that, comprises the following steps:
S1, obtains the model of automobile to be measured;
S2, searches for the car data identical with automobile model to be measured in a database according to the model of automobile to be measured, obtains the first Search Results;
S3, in described first Search Results, search meets the vehicle of first condition and second condition respectively, and calculates the maintenance expense X of automobile to be measured based on the vehicle age and the maintenance expense Y based on vehicle mileage according to the vehicle meeting first condition and second condition respectively;
If need predict the maintenance expense of automobile to be measured in following n month, and the history of setting out on a journey of described automobile to be measured is m month, then described first condition refers to set out on a journey the vehicle that history is greater than m+n month;
If what automobile to be measured was current set out on a journey, mileage is L1, need predict that automobile to be measured is in the maintenance expense of mileage needed for L1 to L2, then the described second condition mileage that refers to set out on a journey is greater than L2, and all makees the vehicle of maintenance at the mileage points L4 being greater than L2 and the mileage points L3 that is less than L1;
S4, merges X and Y, obtains the maintenance expense that described vehicle to be predicted is required in following n month.
2. a kind of auto repair upkeep cost Forecasting Methodology according to claim 1, it is characterized in that, described step S1 also comprises: the year built obtaining automobile to be measured;
Described step S2 also comprises: search for the same model vehicle identical with the automobile making time to be measured in a database according to the year built, and judges whether the vehicle fleet size searched reaches threshold value t, if do not reach, then reformulates search condition search; If reach, then perform S3.
3. a kind of auto repair upkeep cost Forecasting Methodology according to claim 2, it is characterized in that, described in reformulate search condition and refer to: with the described immediate year built in Rail car manufacture time to be predicted or reformulate search condition with the model of the immediate vehicle of described vehicle model to be measured.
4. a kind of auto repair upkeep cost Forecasting Methodology according to claim 1, is characterized in that, calculates automobile to be measured be specially based on the maintenance expense X at vehicle age in described step S3 according to the vehicle meeting first condition:
Obtain each vehicle meeting first condition the maintenance expense summation xi of m+1 month to m+n month, i for described in meet the numbering of the vehicle of first condition;
To the m+n maintenance expense summation xi of individual month, weighted mean value was got at m+1 month to all vehicles meeting first condition, obtains X;
Calculate automobile to be measured according to the vehicle meeting second condition in described step S3 to be specially based on the maintenance expense Y of vehicle mileage:
Obtain each vehicle meeting second condition at the maintenance expense of mileage points L3 and L4 and yj;
The maintenance expense of automobile to be measured based on mileage is calculated in the maintenance expense of mileage points L3 and L4 and yj: Yj=yj* (L2-L1)/(L4-L3) according to each vehicle meeting second condition;
The maintenance expense Yj based on mileage of the automobile to be measured calculated at maintenance expense and the yj of mileage points L3 and L4 the vehicle meeting second condition according to each gets weighted mean value, obtains Y.
5. a kind of auto repair upkeep cost Forecasting Methodology according to claim 1, it is characterized in that, described step S4 is specially:
Get mean value or the weighted mean value of X and Y, as the maintenance expense that described vehicle to be predicted is required in following n month.
6. an auto repair upkeep cost prognoses system, is characterized in that, comprising:
Acquisition module, for obtaining the model of automobile to be measured;
First search module, for searching for the car data identical with automobile model to be measured in a database according to the model of automobile to be measured, obtains the first Search Results;
Second search module, in described first Search Results, search meets the vehicle of first condition and second condition respectively, and calculates the maintenance expense X of automobile to be measured based on the vehicle age and the maintenance expense Y based on vehicle mileage according to the vehicle meeting first condition and second condition respectively;
If need predict the maintenance expense of automobile to be measured in following n month, and the history of setting out on a journey of described automobile to be measured is m month, then described first condition refers to set out on a journey the vehicle that history is greater than m+n month;
If what automobile to be measured was current set out on a journey, mileage is L1, need predict that automobile to be measured is in the maintenance expense of mileage needed for L1 to L2, then the described second condition mileage that refers to set out on a journey is greater than L2, and all makees the vehicle of maintenance at the mileage points L4 being greater than L2 and the mileage points L3 that is less than L1;
Fusion Module, for being merged by X and Y, obtains the maintenance expense that described vehicle to be predicted is required in following n month.
7. a kind of auto repair upkeep cost prognoses system according to claim 6, is characterized in that, described acquisition module also for, obtain the year built of automobile to be measured;
Described first search module also for, search for the same model vehicle identical with the automobile making time to be measured in a database according to the year built, and judge whether the vehicle fleet size that searches reaches threshold value t, if do not reach, then reformulates search condition search; If reach, then search for the second search module.
8. a kind of auto repair upkeep cost prognoses system according to claim 7, it is characterized in that, described first search module also for: with the described immediate year built in Rail car manufacture time to be predicted or reformulate search condition with the model of the immediate vehicle of described vehicle model to be measured.
9. a kind of auto repair upkeep cost prognoses system according to claim 6, is characterized in that, described second search module also for:
Obtain each vehicle meeting first condition the maintenance expense summation xi of m+1 month to m+n month, i for described in meet the numbering of the vehicle of first condition;
To the m+n maintenance expense summation xi of individual month, weighted mean value was got at m+1 month to all vehicles meeting first condition, obtains X;
With
Obtain each vehicle meeting second condition at the maintenance expense of mileage points L3 and L4 and yj;
The maintenance expense of automobile to be measured based on mileage is calculated in the maintenance expense of mileage points L3 and L4 and yj: Yj=yj* (L2-L1)/(L4-L3) according to each vehicle meeting second condition;
The automobile to be measured calculated at maintenance expense and the yj of mileage points L3 and L4 the vehicle meeting second condition according to each gets weighted mean value based on the maintenance expense Yj of mileage, obtains Y.
10. a kind of auto repair upkeep cost prognoses system according to claim 6, is characterized in that, described Fusion Module also for, get mean value or the weighted mean value of X and Y, as the maintenance expense that described vehicle to be predicted is required in following n month.
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| CN106056221A (en) * | 2016-05-27 | 2016-10-26 | 大连楼兰科技股份有限公司 | FP-Tree sequence pattern mining and fault code classification-based vehicle remote diagnosis and spare part retrieval method |
| CN106204793A (en) * | 2016-06-30 | 2016-12-07 | 大连楼兰科技股份有限公司 | Vehicle maintenance predictor method |
| CN107145968A (en) * | 2017-04-13 | 2017-09-08 | 河海大学常州校区 | Photovoltaic apparatus life cycle cost Forecasting Methodology and system based on BP neural network |
| CN107527103A (en) * | 2016-06-21 | 2017-12-29 | 艾玛迪斯简易股份公司 | For excavating the data warehouse of search query log |
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